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Review of State Estimation and Remaining Useful Life Prediction Methods for Lithium–Ion Batteries
The accurate estimation of the state of charge, the state of health and the prediction of remaining useful life of lithium–ion batteries is an important component of battery management. It is of great significance to prolong battery life and ensure the reliability of the battery system. Many researchers have completed a large amount of work on battery state evaluation and RUL prediction methods and proposed a variety of methods. This paper first introduces the definition of the SOC, the SOH and the existing estimation methods. Then, the definition of RUL is introduced, and the main methods are classified and compared. Finally, the challenges of lithium–ion battery state estimation and RUL prediction are summarized, and the direction for future development is proposed.
Review of State Estimation and Remaining Useful Life Prediction Methods for Lithium–Ion Batteries
The accurate estimation of the state of charge, the state of health and the prediction of remaining useful life of lithium–ion batteries is an important component of battery management. It is of great significance to prolong battery life and ensure the reliability of the battery system. Many researchers have completed a large amount of work on battery state evaluation and RUL prediction methods and proposed a variety of methods. This paper first introduces the definition of the SOC, the SOH and the existing estimation methods. Then, the definition of RUL is introduced, and the main methods are classified and compared. Finally, the challenges of lithium–ion battery state estimation and RUL prediction are summarized, and the direction for future development is proposed.
Review of State Estimation and Remaining Useful Life Prediction Methods for Lithium–Ion Batteries
Jiahui Zhao (author) / Yong Zhu (author) / Bin Zhang (author) / Mingyi Liu (author) / Jianxing Wang (author) / Chenghao Liu (author) / Xiaowei Hao (author)
2023
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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